Speaker 1: Extension two twenty four. Give her a call. That number
Speaker 1: again is nine oh nine eight A nine eight three
Speaker 1: seven seven extension two twenty four.
Speaker 2: You're listening to the Inland Talk Express ten fifty AM
Speaker 2: and one oh six point five FM CACAA, Loma, Linda.
Speaker 3: The information economy has a rid. The world is teeming
Speaker 3: with innovation as new business models reinvent every industry industry.
Speaker 3: Inside Analysis is your source of information and insight about
Speaker 3: how to make the most of this exciting new era.
Speaker 3: Learn more at inside analysis dot Comsideanalysis dot com. And
Speaker 3: now here's your host, Eric Kavanaugh.
Speaker 4: All Right, ladies and gentlemen, Hello and welcome back once
Speaker 4: again to the only coast to coast radio show that's
Speaker 4: all about the information economy. It's time for Inside Analysis.
Speaker 4: You're truly Eric Kavanaugh here, and I'm so excited to
Speaker 4: be talking about engineering these days, engineering for success. And
Speaker 4: what's one of the hot topics that we have, especially
Speaker 4: with all these electric vehicles and hybrid vehicles these days,
Speaker 4: are the batteries. And batteries of course are expensive, they're heavy.
Speaker 4: We want them to run as long as humanly possible.
Speaker 4: No one likes charging their batteries up people like using
Speaker 4: their batteries, and certainly in the electric vehicle world, they
Speaker 4: are crucially important. I mean, with a hybrid, you got gas.
Speaker 4: With an electric vehicle, all you have is the electricity
Speaker 4: and that's in the battery. It's very heavy, it's very expensive.
Speaker 4: So what can we do to improve the performance of
Speaker 4: these batteries. Well, guess what we've got. Jovanni Rossi calling
Speaker 4: it all the way from Milan, Italy, my favorite country
Speaker 4: in the world. I love Italy, and he is with
Speaker 4: a company called Electra. They are leaders and applied AI
Speaker 4: for battery packs. I'm like, what explain this? So, Giovanni,
Speaker 4: welcome to the show. Tell us a bit about what
Speaker 4: you folks are doing and how you're able to use
Speaker 4: a digital twin of batteries to improve the performance, and
Speaker 4: not just in the design phase, like meaning for the
Speaker 4: next set of batteries to come on the market, but
Speaker 4: for batteries that are already out there being used. Tell
Speaker 4: us what Electra is and how you're doing this stuff.
Speaker 5: Thank you, Eric.
Speaker 6: So, Electra is an AI clintech and B to B
Speaker 6: software company that is like focused to unlock the full
Speaker 6: potential of Barry technology. So basically as was mentioning. We
Speaker 6: have two different type of products. The first one is
Speaker 6: like a digital tween applications, so we study the barries,
Speaker 6: we understand what is happening, you know, between the berry
Speaker 6: with having a virtual replica of the battery where you
Speaker 6: can thanks to our software capability and AI models, test
Speaker 6: and innovate with the berry, so you can insert your
Speaker 6: different parameters. You can test out new chemistries or new
Speaker 6: models what you want to do, what is necessary to
Speaker 6: make a new berries, and so you can get faster
Speaker 6: to your results. Basically, you know, when you need to
Speaker 6: produce a new berries, it can take up to ten
Speaker 6: years and cost like up to one billion dollars, and
Speaker 6: so of course you need technology as ours to decrease
Speaker 6: of course your time and also your cost into making
Speaker 6: new berries.
Speaker 5: On the other side, we also have a set of
Speaker 5: software for.
Speaker 6: Like you know, managing, optimizing and controlling very capabilities and
Speaker 6: so improving again the performance of the berries and providing
Speaker 6: valuable insights for the different type of users and for
Speaker 6: the business managing these barries so to make the battery
Speaker 6: lasts longer and perform better and so maximizing of course
Speaker 6: the reternal investment for the battery assets.
Speaker 4: Yeah, that's really interesting. So and let's talk first about
Speaker 4: the digital twin and that environment where you have built
Speaker 4: out in a computing environment. Obviously the battery itself, what
Speaker 4: goes into it, and then you're able to allow your
Speaker 4: engineers to play around with different settings. Is it getting
Speaker 4: into the material science? Like how much detail can you
Speaker 4: give us on what's done in that environment and how
Speaker 4: you're enabling more efficient battery designs.
Speaker 6: Ye, can go into more than one hundred parameters, you know,
Speaker 6: into the very spects, so it can be either at
Speaker 6: a cell level and so like you know, into the
Speaker 6: small great stuff in that sense, into until like the
Speaker 6: very pack. So it depends of course of what we
Speaker 6: want to do. But we have different applications, different modules
Speaker 6: in the platform, and so you can go sell to
Speaker 6: pack to pack levels so that you can test and
Speaker 6: innovate whatever is like necessary for you. Of course you
Speaker 6: can change parameters. There are already some pre built AI
Speaker 6: model that you know, we have already designed and tested
Speaker 6: with different customers and understanding of course the trends in
Speaker 6: the market. But then of course we can also create
Speaker 6: some additional customized AI models to do some very specific
Speaker 6: operation that a customer may be doing.
Speaker 4: That's interesting. So with all these parameters, you're allowing the
Speaker 4: scientists basically to get in there and just play around
Speaker 4: with things and see about whether it's the kind of
Speaker 4: materials that are used, how much of them are used,
Speaker 4: all these different aspects of the actual design process. And
Speaker 4: then in this digital twin world you can kind of
Speaker 4: play around. Can you talk a bit about the accuracy,
Speaker 4: like what, you know, what's the range of efficacy with
Speaker 4: these tests when you do them in the digital twin
Speaker 4: world and then you see what happens in the real world.
Speaker 4: Is that something that you're capturing and managing over time
Speaker 4: the data around that.
Speaker 6: Yeah, I mean like you can play around with whatever
Speaker 6: it's like, you know, within the platform of course, and
Speaker 6: also adding some additional bodiles and piece when it is necessary.
Speaker 5: And I think that the main benefit.
Speaker 6: Here is like you know, when you need to test
Speaker 6: out some berries, you know, and you can have this
Speaker 6: digital replica and you can play around with it, you
Speaker 6: can decrease the testing time, so you know that that
Speaker 6: seems like a real deal. And the game changer perspective
Speaker 6: that is, like you know, you don't need to have
Speaker 6: like a lot of you know, physical testing, but you
Speaker 6: can do everything virtually and this can go up to
Speaker 6: you know, ninety percent of the testing can be reduced,
Speaker 6: of course in the best case scenario, but we have
Speaker 6: seen that an average can be of like reduction in
Speaker 6: test is like sixty to seventy percent, so meaning that
Speaker 6: of course you're saving up a lot of time and
Speaker 6: also a lot of money, and so the scientists can
Speaker 6: also focus on some more interesting stuff. Let's say this
Speaker 6: way than just replicate the test over and over again.
Speaker 4: You know, I remember talking to a gentleman, a very
Speaker 4: very interesting guy. He is an electric car racer, and
Speaker 4: we were over in Belgium for an electric car race
Speaker 4: a couple of years ago, and he was talking about
Speaker 4: this this effect when you float you can actually slowly
Speaker 4: you can actually increase the energy in the battery a
Speaker 4: little bit. Do you know about this? Like when you
Speaker 4: break our cost in an EV, the electric motor access
Speaker 4: kind of a generator converting the energy back into electrical energy.
Speaker 4: Is that right correct?
Speaker 5: Yeah? And you can say up plum energy and refiel
Speaker 5: that of the marry.
Speaker 4: Yeah, that's pretty cool. That's pretty cool stuff. And then
Speaker 4: talk about the battery management systems I'm getting spanked by
Speaker 4: the sunlight here in my head, but try not to
Speaker 4: worry about that. Talk about how you can also work
Speaker 4: with the management matteries I'm sorry, the battery management system
Speaker 4: to improve performance there. What does that look like?
Speaker 6: So basically, let's take the an electric vehicle as an example.
Speaker 6: You know what you do right now, It's like you
Speaker 6: have different information that are coming from the berries, coming
Speaker 6: from the driving style, coming from the environments, and of
Speaker 6: course all of these is like impacting the performance of
Speaker 6: the battery.
Speaker 5: So what we do is like we take all of.
Speaker 6: These information together, so berries, environment, driving style, and we
Speaker 6: put everything together, and thanks to our AI you know, algorithm,
Speaker 6: we can provide an output to the users, to the
Speaker 6: drivers in the sense that is like how you can optimize,
Speaker 6: of course your range. You can so extend your range
Speaker 6: in this sense we have been able to estimate that.
Speaker 6: Of course, the reduction of sorry, the average error in
Speaker 6: like you know, estimating the range is like twenty percent
Speaker 6: fifteen to twenty percents depend on vehicles. And with our
Speaker 6: model you can go down to less than one percent
Speaker 6: error in estimating range. So this means that of course
Speaker 6: you have much more precise information in that space, and
Speaker 6: we also you know, and with additional insights and suggest
Speaker 6: that we can provide, you can also extend the range
Speaker 6: and the capabilities. We have actually been testing these very
Speaker 6: recently in a cross country you know trip coming starting
Speaker 6: from Boston to Vegas, because we were presenting our technology
Speaker 6: at CS twenty thirty five, and we did it with
Speaker 6: like a Tesla cybertruck. So our founder, a core founder
Speaker 6: and CEO of Britain Martini, and one Copio drove all
Speaker 6: the way down from Boston to Vegas to demonstrate the
Speaker 6: capability for all our algorithms.
Speaker 4: That's Alloyd.
Speaker 5: Yeah, it's a very long drive.
Speaker 6: They looked like seven days, so that's really intense sad
Speaker 6: this way, and we have been able to you know,
Speaker 6: showcase and demonstrate that we can extend the range of
Speaker 6: twenty percent and reduce the charges of around thirty three percent.
Speaker 5: That is pretty it's a pretty good number, we say.
Speaker 6: And in all of these you know, also managing and
Speaker 6: understanding all the data coming from the various environmental driving styles,
Speaker 6: and of course we have also capability to predict failures
Speaker 6: in advance, so we can have like in a product
Speaker 6: that is like one hundred percent safe for of course
Speaker 6: the driver and and for them of course vehicle producer
Speaker 6: in that sense that is also very very interesting, and
Speaker 6: all of these can be scaled when you think not
Speaker 6: only you know individual drivers, but you have like fleets
Speaker 6: of vehicles or flit of electric vehicles, you know, adding
Speaker 6: all of these information and understanding what is happening within
Speaker 6: the so of course, what is like the state of
Speaker 6: hels of your battery, what is like the degradation of
Speaker 6: your batteries, and so how much your btrier can stay
Speaker 6: can last? You know, for what we define as the
Speaker 6: remain a useful life of a battery, you can actually
Speaker 6: increase the value of your asset up to forty percent.
Speaker 6: So that is like a way for both the drivers,
Speaker 6: the OEM or the battery producer. And then you know,
Speaker 6: the fleet operators to have many information, many sites and
Speaker 6: so have a much better capability to manage the operations
Speaker 6: and so of course get the value out of it.
Speaker 4: Yeah, well for sure. So I'm guessing what you're doing
Speaker 4: here is in terms of being able to optimize the
Speaker 4: battery life is you're you're analyzing the driving behavior. So
Speaker 4: does this person accelerate very quickly the drive? It very
Speaker 4: high speeds on a regular basis, do they go up
Speaker 4: and down often in terms of speed. These sorts of
Speaker 4: variations will have an impact on the battery life, and
Speaker 4: so what you can probably do is detect those algorithmically,
Speaker 4: understand what's really happening, and then give some recommendations to
Speaker 4: the user to say, hey, maybe if you were to
Speaker 4: not speed up and slow down so quickly, you would
Speaker 4: extend I'm just guessing here that you know, if you
Speaker 4: haven't even keel drive, you're going to get better use
Speaker 4: of the battery.
Speaker 6: Right, yes, and your mattery can last longer because it's
Speaker 6: not allly about range and performance, but it's like you know,
Speaker 6: if you drive better that sense, so if you use
Speaker 6: it battery better, your barrier can last for longer. Of course,
Speaker 6: we have some algorithms like optimizing how much the bettery
Speaker 6: can last. But of course then in this case, of
Speaker 6: course also the users like responsible of that. So you know,
Speaker 6: the two things combined that is like what can make
Speaker 6: you very lasts longer? And if you very lasts longer,
Speaker 6: you know, it means that you are keeping your cart
Speaker 6: longer in a way, and that's also saving money in
Speaker 6: a sense, but also results of some environmental impact that
Speaker 6: is definitely important in this sense. And of course you
Speaker 6: can scale these not only to electric vehicles, but if
Speaker 6: you think also stationary storage, that is like another application,
Speaker 6: usually much more for B to B side, let's say
Speaker 6: this way of leg for businesses, but it's also becoming
Speaker 6: quite popular for homes, especially you know, mentioning you know
Speaker 6: the B two B side, or like businesses, when you
Speaker 6: have some solar farms or wind farms and you want
Speaker 6: to store the excess of energy because you know, renewable
Speaker 6: energy is very cool, but you know the shine and
Speaker 6: the sun, sorry, it is not always shining and the
Speaker 6: wind is not always ploying, so you need to you know,
Speaker 6: to store the energy when when you have the excess
Speaker 6: of energy and then release it into the grid. And
Speaker 6: so having technological capabilities and aid I driven capability to
Speaker 6: understand what is happening in these berries, to manage them
Speaker 6: better and to optimize them, and also to predict falls
Speaker 6: because you know, having all of these information and it
Speaker 6: is one of the main game change capability about what
Speaker 6: technology is that we can predict falls up to three
Speaker 6: months in advance. So this means that we can understand
Speaker 6: what is actually happening, and you know how your berry
Speaker 6: is performing, and if you're going to have some critical faults,
Speaker 6: and when I mean criptical faults is like you know
Speaker 6: them a runaway or like fires or some other issue
Speaker 6: with a very that's very very important to maintain safety
Speaker 6: and reliability of that asset.
Speaker 4: Yeah, no, that that's very interesting. And there are also
Speaker 4: these power walls, right, doesn't Tesla don't they have a
Speaker 4: power wall? So basically you can buy a car, but
Speaker 4: you also buy this wall that is like in your house,
Speaker 4: and it's it's it is a long term battery storage
Speaker 4: is what it boils down to.
Speaker 5: Right.
Speaker 4: It allows you to I'm guessing to charge the car
Speaker 4: faster and do things of this nature. Yeah, you also
Speaker 4: just have a power source, like you mentioned, because I
Speaker 4: looked into solar panels years ago when we were in Texas,
Speaker 4: and you know, some of the key things you have
Speaker 4: to keep in mind are being able to manage that
Speaker 4: power that comes in and storage such that at a
Speaker 4: certain point you can then pump it back into the grid, right,
Speaker 4: which is you know, you have to be very delicate
Speaker 4: about that obviously, because it's electrical energy. You don't want
Speaker 4: to to get too much So what are you able
Speaker 4: to do there in terms of understanding the sort of
Speaker 4: dynamics of how battery usage works in these walls, and like,
Speaker 4: can you I presume you can extend their life too, right.
Speaker 5: Correct, we can extend the life.
Speaker 6: You can monitor them of course better and especially understanding
Speaker 6: how they are performing and increase their again performance and
Speaker 6: optimizing them so that you can have like more energy
Speaker 6: to use in that sense, and charging faster your car
Speaker 6: if it is a car for application for a car,
Speaker 6: or like you know, managing better d energy for your home.
Speaker 6: Optimizing of course also the a flow of energy because
Speaker 6: usually you have pav panels and so you want to
Speaker 6: have everything that is working in a very optimized way.
Speaker 4: That's very interesting. So do you get much traction in
Speaker 4: that space, or maybe talk a bit about your clients
Speaker 4: who's using this right now? To what effect?
Speaker 5: I mean?
Speaker 4: Obviously it's you're just extending the life of this asset,
Speaker 4: and you're improving the use of this asset, which is
Speaker 4: incredibly valuable and saves money and also saves you on trouble,
Speaker 4: especially this part about being able to predict fires, because
Speaker 4: that is the one real big downside, it seems to
Speaker 4: me of this whole paradigm is that these things can
Speaker 4: get too hot and just blow up and that there's
Speaker 4: no bueno, right, so what can you do happen? Yeah, so.
Speaker 6: We I mean like thanks to the technology that that
Speaker 6: is like a new layer I would say about understanding
Speaker 6: better these assets and using them better. I think that
Speaker 6: you know right now that are the right technology and
Speaker 6: also the artificial intelligence and machine learning capabilities to better
Speaker 6: understand how to again manage and optimize these assets.
Speaker 5: That will be also.
Speaker 6: You know, well we'll have we will have so much
Speaker 6: a broader adoption of these assets. And so this will
Speaker 6: also change the paradigm of how we think at energy
Speaker 6: in general and also the all the energy transition and
Speaker 6: everything is based on this because of course it's not
Speaker 6: only again the renewables, but you also need some other
Speaker 6: components to make this happen and also to sustain a
Speaker 6: much lower environmental impact, and so all of these will
Speaker 6: be much more necessary, especially technological side, for like making
Speaker 6: this transition you know, a reality. And if you think
Speaker 6: out the grid right now is like you know configure
Speaker 6: for instance, you know and you have like you know,
Speaker 6: the traditional system in the western countries, like you have
Speaker 6: big plants where you produce the energy and then it's
Speaker 6: distributed to like you know, to the grid, to different facilities.
Speaker 6: That is like off taking the energy right now with
Speaker 6: having you know, solar farms, wind farms and also you
Speaker 6: know BV panels in like domestic and business facility, so
Speaker 6: like for reastance on your rooftop or like you know,
Speaker 6: in some olar situations, you basically are changing this perspective
Speaker 6: and changing the paradigm of how the energy flows are
Speaker 6: actually you know, pumping in energy into the grid. And
Speaker 6: so you need again capabilities technological capabilities to keep everything
Speaker 6: working and running smoothly and also to have additional benefit
Speaker 6: to all the users involved.
Speaker 4: What's next? What are you guys working on now that
Speaker 4: we can expect in like a year or so. What's
Speaker 4: the next big push for you? Oh?
Speaker 6: I cannot share that I would like to, but that's
Speaker 6: a good Yeah, that's pretty funny.
Speaker 5: It is like yeah, yeah, I mean I can share.
Speaker 6: It's like, you know, we are implementing day after day
Speaker 6: our models and you know, understanding what are the new
Speaker 6: needs of the customer. And I think the most important
Speaker 6: stuff this is also part of my job is like
Speaker 6: getting ahead of the curve. So it's like what trends
Speaker 6: are going to be there in the next six, twelve,
Speaker 6: eighteen months and what we can do to address those
Speaker 6: in advanced So one of the things that we've always
Speaker 6: been able so is like understanding the trends before they
Speaker 6: happen and you know, getting something out before that moment
Speaker 6: so that we be leading.
Speaker 4: Well, this is fantastic. And the company is Elektra. That
Speaker 4: are you based in Italy or you're just in Italy?
Speaker 4: It's a company based in Italy.
Speaker 6: No, the company is based in Boston, Massachusetts, so the
Speaker 6: main edportter is there and then we also second European
Speaker 6: headquarter into in Italy.
Speaker 4: Excellent, Well, that sounds going to be great, great, great,
Speaker 4: great information, great content. I love what you guys are doing.
Speaker 4: Very cool. Look these guys up, Giovanni Rossi of Elektra.
Speaker 4: We'll be right back. You're listening to Inside Analysis.
Speaker 3: Welcome back to Inside Analysis. Here's your host, Eric Tavanaugh.
Speaker 4: All right, folks back here on Inside Analysis. Your host here,
Speaker 4: Eric Kavanaugh, and I'm very pleased to have another guest
Speaker 4: with us today. Timothy Baines is the founder of a
Speaker 4: company called Compatio, doing really interesting stuff they've built a
Speaker 4: whole platform for you could say configure price quote. Really
Speaker 4: it's a platform for managing compatibility hence the name of
Speaker 4: different products. So think electrical engineering or building materials, or
Speaker 4: anytime you need to bring a bunch of big pieces
Speaker 4: and parts together to create some whole like I think
Speaker 4: a car, for example, or any kind of complex machinery.
Speaker 4: You need to know are these parts compatible, do they
Speaker 4: work together? And then understand pricing and be able to
Speaker 4: figure out what to charge for these things. And they
Speaker 4: have a whole platform that does that, and they're focused
Speaker 4: on a couple of markets, but they can do all
Speaker 4: kinds of different stuff. But Timothy, what really struck my
Speaker 4: interest here is you mentioned this taxonomy, and if you would,
Speaker 4: can you tell us what you mean by a taxonomy
Speaker 4: and how does that fit into what Campatio does.
Speaker 7: Yeah, sure, Eric, So, first of all, I think it's
Speaker 7: important to understand that at the core, at the heart
Speaker 7: of our system is essentially a knowledge graph, and that
Speaker 7: knowledge graph sits on top of a decision taxonomy that
Speaker 7: within a given industry, let's say electrical switch gear for example,
Speaker 7: or industrial automation components, we have a very precise set
Speaker 7: of categories as far as what components are what, along
Speaker 7: with very high quality and very carefully engineered attributes on
Speaker 7: the products as well as product catalogs for different manufacturers.
Speaker 7: So together the taxonomy which is the categories and the attributes,
Speaker 7: as well as the rules on what the attributes are
Speaker 7: allowed to be, what data type, what is the range
Speaker 7: or even what is the picklist of values, that comprises
Speaker 7: the taxonomy, and then that gets hooked up to catalogs
Speaker 7: of products, and together those feed into our larger sort
Speaker 7: of layers of logic that is essentially a knowledge graph.
Speaker 5: Yeah.
Speaker 4: And the reason why that's important for our audience out
Speaker 4: there listening is that when you have this graph and
Speaker 4: you have this ontology, you are cataloging all these myriad
Speaker 4: parts of which there can be millions of different ones,
Speaker 4: in such a way as to facilitate the alignment over time.
Speaker 4: Because if you were to try to manually put those
Speaker 4: into a just traditional relational database or something, first of all,
Speaker 4: it would take forever a day, and second of all,
Speaker 4: it would not be performed at all, it would be
Speaker 4: very very slow, very painful. So by going this route
Speaker 4: of having a graph, a knowledge graph substrate with ontologies
Speaker 4: for particular industries or disciplines, if you will, you're really
Speaker 4: facilitating the end use of this whole system to allow
Speaker 4: people to compare and contrast and figure out do these
Speaker 4: parts work together or not. That's very very clever. And
Speaker 4: you built the graph yourself, You popular this graph. Can
Speaker 4: you talk about some examples of how the graph and
Speaker 4: the ontologies facilitate what your end users want?
Speaker 7: Yeah, absolutely, so if you think about well, if you
Speaker 7: think about any type of product that's available at scale,
Speaker 7: sort of on a large e commerce site, let's say
Speaker 7: all products need to have effective attribution and descriptions to
Speaker 7: power search. So that's kind of a fundamental use case.
Speaker 7: But when we get into more complex verticals, more complex
Speaker 7: product domains, where the attributes the specifications of the products
Speaker 7: need to be very very precise, you need an entirely
Speaker 7: different level of quality and accuracy on those attributes. And
Speaker 7: the use cases that that powers are things like search.
Speaker 7: But you know search, including parametric search, right, so you
Speaker 7: can filter down and find exactly what you want, so
Speaker 7: any kind of discovery. But then the higher order logic
Speaker 7: that also that that enables in the domains where we
Speaker 7: operate the products are complicated. Finding the right product for
Speaker 7: your specific application. Even just a single component can be
Speaker 7: non trivial, and oftentimes you may need to talk with
Speaker 7: an expert, let's say, that can help you figure out
Speaker 7: what the right product is. And then if you think
Speaker 7: about a product that is part of a larger system,
Speaker 7: an industrial automation system with a motor and a motor
Speaker 7: controller and a relay and so forth, at all need
Speaker 7: to go together to be able to identify those components
Speaker 7: number one that fit your application and the number two
Speaker 7: go together. That's non trivial, and historically that task has
Speaker 7: been handled by experts, folks with ten twenty thirty forty
Speaker 7: years of experience in that industry, and you'd walk to
Speaker 7: the counter and a distributor, a dealer, a store, or
Speaker 7: they'd come to your plant and they'd have all the
Speaker 7: expertise that's needed to help you speck it out. But
Speaker 7: things are going online. Obviously, digital commerce is expanding, and
Speaker 7: we also are seeing some fairly significant turnover in the
Speaker 7: industry in terms of the labor force, where a lot
Speaker 7: of the folks that came in twenty thirty forty years ago,
Speaker 7: they're retiring their baby boomers and they're rolling out and
Speaker 7: it's what our the companies that we're working with are
Speaker 7: telling is it's hard to find the right people, and
Speaker 7: it's hard to train them, and it's hard to retain them.
Speaker 7: So we see really an emerging and a massive knowledge
Speaker 7: gap coming into as digital commresce becomes more more prominent.
Speaker 7: Combined with this problem of expertise, it makes it very
Speaker 7: difficult to do these kinds of things to find the
Speaker 7: right product fit your application, make sure they go together,
Speaker 7: and then price them and quote them and get them
Speaker 7: out the door. So we're addressing all of those use cases.
Speaker 4: Yeah, it's really impressive. And when you talk about industrial
Speaker 4: engineering the machines that make machines, basically you have years
Speaker 4: on these things. Different models and things change your over year.
Speaker 4: I mean, I'll use the metaphor of an automobile just
Speaker 4: because most people have an automobile or at least know
Speaker 4: someone who has an automobile, and depending upon the year
Speaker 4: of that make, you'll get different parts for different functions,
Speaker 4: like whether it's to run something in the engine or
Speaker 4: in the electrical system or whatever, and it really matters
Speaker 4: to get the right thing. I've been one of those
Speaker 4: do it yourselfer as I've bought some products I think
Speaker 4: it was going to fix, and it didn't fit the car.
Speaker 4: I was like, oh man, because it was the wrong year.
Speaker 4: So it's really important to have the year right to
Speaker 4: make the model. And then all those attributes you talk
Speaker 4: about are basically edges on the graph, right, So you're
Speaker 4: able to capture all that and then maintain some history
Speaker 4: and thus facilitate finding that one specific part that you
Speaker 4: need to fix this machine.
Speaker 5: Right, Yeah, that's right.
Speaker 7: And you know, taking in a car and automobile as
Speaker 7: an example, I mean that data is generally very carefully
Speaker 7: maintained and curated by the manufacturer of that car. I
Speaker 7: mean even some of the legally required that they maintain
Speaker 7: that data for many types of things, for example liability
Speaker 7: reasons and so forth. But where it gets really interesting
Speaker 7: is where you, let's say, again keeping our focus on
Speaker 7: a car, you get out to what goes on the
Speaker 7: car or around the car, or attaches to the car,
Speaker 7: or accessorizes the car. That data is not maintained by
Speaker 7: the manufacturer. So when you get into these multi brand situations,
Speaker 7: something that goes on or with something else, you're in
Speaker 7: a space that really nobody owns. Right now, where nobody
Speaker 7: owns that data what goes with what? Across brands, and
Speaker 7: in the industries where we operate electrical automation, building materials,
Speaker 7: and so forth, those problems are everywhere. Everywhere, folks are
Speaker 7: trying to figure out what goes with what, especially when
Speaker 7: it's cross brand, but even within a brand, it's still challenging. So, yeah,
Speaker 7: the problem is huge, and the markets are huge. The
Speaker 7: companies in the industries that have these kinds of issues
Speaker 7: are huge. We're talking trillions of dollars worth of annual
Speaker 7: revenues in these industries and they all suffer from these problems.
Speaker 4: Yeah, well you can just imagine. I mean, I've studied
Speaker 4: supply chain through SAP and some other companies, and it
Speaker 4: gets incredibly complicated because some of these companies will have
Speaker 4: hundreds of thousands, even millions of SKUs and so you know,
Speaker 4: just from a data management and storage perspective, that's difficult.
Speaker 4: Then enabling search is difficult. Then enabling search for your
Speaker 4: partners and for your clients, which is also very important
Speaker 4: because you want them to be able to find the
Speaker 4: bits and pieces, the products, the component parts they need,
Speaker 4: and all of that is facilitated by this compatible platform right.
Speaker 5: That's right.
Speaker 7: Yeah, Search and discovery, search discovery, compatibility and configuration.
Speaker 4: And then right once you can search and align and
Speaker 4: determine compatibility, then you get into things like pricing and
Speaker 4: configure price quote, which is another huge part of the industry.
Speaker 4: Knowing what you can charge for something, Knowing what the
Speaker 4: margins are that your competitors are putting out in the marketplace,
Speaker 4: that you're putting out there, finding the balance amongst all
Speaker 4: those things. I mean, that's a full time job for people.
Speaker 4: And you still have to take guesses, right, I mean,
Speaker 4: you're going to figure out all your prices, You're going
Speaker 4: to configure and figure out Okay, I think I can
Speaker 4: get this price, and you send it out there and
Speaker 4: hopefully they bite, and then you go ahead and go
Speaker 4: through this, and you have to be able to go
Speaker 4: back and audit that and report on it and figure
Speaker 4: out where's the margin because you've got to make money, right,
Speaker 4: Everyone has to make money at the end of the day.
Speaker 4: So you're helping facilitate these transactions. You're helping make sure
Speaker 4: that companies are getting the parts that they need to get,
Speaker 4: and you're also I think enabling more of that sharpening
Speaker 4: of the pencil basically, because once you know what the
Speaker 4: margins are, and once you know what the reliability of
Speaker 4: the providers are as well, then you can make these
Speaker 4: informed decisions. Then you can kind of understand where the
Speaker 4: margin is and that's kind of how you grow your business.
Speaker 4: But folks, so Compatio co O m P A t
Speaker 4: io is Acompatio dot com. Is that right, Ai, Compatio
Speaker 4: dot Ai. Well, Timothy, congratulations on building this platform. This
Speaker 4: is a pretty seriously big deal and you're really greasing
Speaker 4: the tracks that I love that you've got a taxonomy
Speaker 4: or taxonomies ontologies basically and a knowledge graph. That's the
Speaker 4: way to do it. That's what our audience loves to
Speaker 4: hear about. So thanks for your time today. Look you
Speaker 4: spokes up online ladies and gentlemen. Compatio dot ai and
Speaker 4: Timothy Bains will talk to you next time. You've been
Speaker 4: listening to Inside Analysis.
Speaker 3: Welcome back to Inside Analysis. Here's your host, Eric Tavanaugh.
Speaker 4: All right, folks back here on Inside Analysis. Your host here,
Speaker 4: Eric Haavadaugh, And today I'm with Abby Rass of a
Speaker 4: company called Hydraulics. It's hyd Elix, and Hydraulics is a
Speaker 4: very interesting company that is filling a significant gap that
Speaker 4: is enabling really powerful cloud content delivery networks and other
Speaker 4: companies to do real time analysis of observability at scale.
Speaker 4: And why does that matter. It matters for a lot
Speaker 4: of different reasons. It matters because these systems are very
Speaker 4: complex these days. You can imagine Netflix, for example, the
Speaker 4: amount of data that's flying out all these various sources,
Speaker 4: all these various data centers to get to your TV
Speaker 4: so you can watch all these movies. That's a lot
Speaker 4: of data. So when you're trying to maintain these systems,
Speaker 4: you're looking for all kinds of things. And when you're
Speaker 4: trying to do that sort of industrial grade analytics on
Speaker 4: systems performance and management, well there are lots and lots
Speaker 4: of metrics that you can look for these days. And
Speaker 4: obviously the Kubernetes movement has played deep into this whole
Speaker 4: space now spinning up amazing amounts of compute, federating compute basic,
Speaker 4: so really enabling this tremendous scalability of information systems of
Speaker 4: enterprise create information systems. But also it opened all sorts
Speaker 4: of Pandora's boxes on data and on what's actually happening
Speaker 4: under the covers, so to monitor that and to watch
Speaker 4: not just for bad guys, but also for performance issues,
Speaker 4: anything that stumbles and starts causing trouble to users. You
Speaker 4: need to absorb tons and tons of data from a
Speaker 4: lot of different systems and be able to coalesce that
Speaker 4: and analyze it in fair fairly real time. And that's
Speaker 4: the kind of stuff that Hydraulics does. So they call
Speaker 4: themselves a bit of a streaming data lake company, but
Speaker 4: also headless observability. And I love this headless concept. I'm
Speaker 4: hearing this more and more and I really like it
Speaker 4: because what it kind of implies is, look, we're giving
Speaker 4: you an engine and you can put whatever covering you
Speaker 4: want on it that suits your particular needs or your
Speaker 4: environment to use this powerful engine to get stuff done.
Speaker 8: Right.
Speaker 4: What do you think?
Speaker 9: Yeah, I mean it's like mister potato head.
Speaker 10: You can flip THEMP you can, you know, put on
Speaker 10: different hats, put on different eyes. It's whatever you want
Speaker 10: in the top. But you know the bum right, But no,
Speaker 10: it's it's essentially the same thing.
Speaker 5: Right.
Speaker 10: So with Hydraulics our software, anyone can plug in any
Speaker 10: type of dashboard.
Speaker 9: It comes with pre built Graffona dashboards.
Speaker 10: However, if somebody wants to keep their splunk instance, if
Speaker 10: somebody wants to keep any kind of front end dashboard
Speaker 10: that they have, that's totally fine. We have connectors so
Speaker 10: you can put any front end analytics dashboard on top
Speaker 10: of our back end and still get the benefits of
Speaker 10: less money, especially for storage, longer data retention, always hot data,
Speaker 10: for rapid queering and ingesting massive amounts of data at
Speaker 10: least to terabyte a month in real time.
Speaker 4: Well yeah, and that real time nature is crucial. You
Speaker 4: were telling us a story before we hit the record
Speaker 4: button about a particular use case where a Black Friday
Speaker 4: real retailer was able to discover a problem as it
Speaker 4: was happening. Some kind of a hack was going on,
Speaker 4: found it, discover it, nullified it. Nobody even noticed on
Speaker 4: the front end, right.
Speaker 10: Yeah, this was a large retailer global, It was during
Speaker 10: Black Friday week and there was a massive DDoS attack
Speaker 10: and it spanned eighty countries and three thousand IP addresses.
Speaker 4: Wow.
Speaker 10: Graphic Peak, the manager observability service that akama I built
Speaker 10: using our software at Hydraulics pinpointed this attack instantly, so
Speaker 10: the company was able to mitigate it in real time
Speaker 10: before any of their customers even noticed anything. Wow, So
Speaker 10: it really was instrumental for them to maintain business.
Speaker 4: Yeah, that's amazing. And you think about what it takes
Speaker 4: to be able to ingest all that data, and that's
Speaker 4: a big key to what you're offering, right is number one,
Speaker 4: hyper scale ingestion of data. So you can tap into
Speaker 4: any number of systems, industrial grade systems, cloud infrastructure basically,
Speaker 4: and you are very good at pulling in data very quickly,
Speaker 4: but then also enabling the analysis of that data. And
Speaker 4: that's all in hydraulics, right though you do also, as
Speaker 4: you suggest, you're headless, so you can work in conjunction
Speaker 4: with any number of analytics tools.
Speaker 9: Right, that's absolutely true.
Speaker 10: We actually just ran one of the largest sporting events
Speaker 10: in the country and at the peak viewing time, we
Speaker 10: were ingesting more than a petabyte of data an hour.
Speaker 9: Wow, I'm sorry, not an hour, more than a petabyte.
Speaker 10: We let me repeat that, at peak viewing time, we
Speaker 10: were ingesting more than a petabyte of data a day, Okay,
Speaker 10: which is an enormous I mean that's monster data, right,
Speaker 10: and a lot of a lot of times these legacy systems,
Speaker 10: because they were not built for the modern architecture of
Speaker 10: today will crash or slow.
Speaker 9: Down when ingesting that amount of data.
Speaker 10: But for us a petabite a day, nothing crashed, nothing
Speaker 10: slowed down.
Speaker 9: We take it in in real time. We alert on.
Speaker 10: Ingests, so we can show incidents in real time. You
Speaker 10: can query in three seconds, six seconds, super super fast,
Speaker 10: and then you can go fix the problem before anyone notices.
Speaker 4: Yeah. Well, and you also you do some clever things
Speaker 4: on the storage side and some clever things on data reduction. Basically,
Speaker 4: so you really were purpose built for this kind of
Speaker 4: use case, right, I mean your executive suite they saw
Speaker 4: this coming, they saw a need for this, and then
Speaker 4: purpose built this engine to be able to fill this
Speaker 4: particular use case.
Speaker 5: Right.
Speaker 10: Yes, I mean it's two problems, right. It's the fact
Speaker 10: that the amount of data is growing so much each
Speaker 10: year it's supposed to increase by forty percent, that's when
Speaker 10: an analyst recently told us year over year. And also
Speaker 10: it's the cost. I mean, keeping data like this.
Speaker 9: Costs a fortune. So we wanted to solve both of
Speaker 9: those problems.
Speaker 10: The ability to keep all your data no matter how
Speaker 10: large the data set, and also make it affordable.
Speaker 9: So that's what we're doing.
Speaker 10: And the reason why, one of the biggest reasons with
Speaker 10: especially data retention, that we can make it affordable, well
Speaker 10: is that we have a twenty five to fifty compression ratio,
Speaker 10: so we make that huge data set very very small
Speaker 10: and easy to keep and managed.
Speaker 4: Yeah, now that's clever. What's interesting is there are lots
Speaker 4: of different things that you can do from an architectural
Speaker 4: perspective to optimize for certain goals, for certain objectives, and
Speaker 4: this compression capability you have, that's why you're able to
Speaker 4: enable a much more cost effective storage layer, right.
Speaker 10: Correct, And we have decoupled architecture, so compute and storage
Speaker 10: can be scaled up or scaled down separately.
Speaker 4: Right, which is exactly what Snowflake did, right. I mean,
Speaker 4: I think they were the poster children for separating compute
Speaker 4: from storage. And everyone's like, oh, that's a pretty good idea.
Speaker 4: Let's go ahead and run with that, right.
Speaker 5: Yeah.
Speaker 10: In fact, in that largest football game, we were able
Speaker 10: to save the customer compute power because they didn't you know,
Speaker 10: when the peak viewing time dropped, they didn't need all
Speaker 10: that infrastructure, right, It scaled down really easily while still
Speaker 10: keeping the.
Speaker 4: Storage well, you know, to myself here you talk about
Speaker 4: traffic Peak, which is a cool name, by the way,
Speaker 4: and that's that's the solution which Akamai built using your technology, right,
Speaker 4: which they're now selling to their clients as well. Is
Speaker 4: that correct?
Speaker 10: Yes, we have so I leave partner marketing for Hydraulics,
Speaker 10: and we have very strong partnerships with Akamai and AWS
Speaker 10: and others, but our software can run on any cloud platform.
Speaker 9: Akima is a huge.
Speaker 10: Success story for us because they have been so supportive
Speaker 10: of the partnership from every level, from product sales to marketing.
Speaker 10: So when we launched in twenty twenty three traffic Peak,
Speaker 10: which is the manager observability service that Akamai built using
Speaker 10: our software, we had thirty three customers in the beginning.
Speaker 9: In one year, that number shot up to more than
Speaker 9: three hundred and seventy customers.
Speaker 10: That's great, and you know, it's a lot because the
Speaker 10: product speaks for itself, but also because we have such
Speaker 10: a collaborative relationship with Akamai and they're so supportive on
Speaker 10: all ends, and we're so supportive of them on all ends.
Speaker 10: So it's really been a successful partnership. And they just
Speaker 10: named us the North of America Qualified Compute Partner of
Speaker 10: the Year because it's great well.
Speaker 4: And the thing to understand for from a broader business
Speaker 4: audience perspective is that we are seeing tremendous advances in
Speaker 4: all sorts of different spaces, and companies can come along
Speaker 4: like Hydraulics and build this engine purpose, build this engine
Speaker 4: for massive and jest for real time analysis. And when
Speaker 4: you do that, you're head and shoulders above some of
Speaker 4: the older legacy systems that just weren't designed to do
Speaker 4: that kind of thing. And you know there are a lot,
Speaker 4: you know, so years ago I had doctor Michael Stonebreaker
Speaker 4: on the show. I don't know if you recognize the name,
Speaker 4: but he's the godfather of the modern database. Basically, he
Speaker 4: wrote the Postgress database like fifty years ago and has
Speaker 4: spun up several companies, including Vertica, which in its time
Speaker 4: in two thousand and five was all about real time analytics,
Speaker 4: was all about column oriented analytics. It was a new
Speaker 4: crazy thing back then. But he had this one fun
Speaker 4: quote on a show one day where he said, the
Speaker 4: funny thing is that people should realize eighty percent of
Speaker 4: the code that's been written in the world should just
Speaker 4: be thrown away at this point was that it was
Speaker 4: written for older systems. And so if you think about
Speaker 4: spinning disc for example, versus solid state drive, well, it's
Speaker 4: a very different world if you're using SSDs in terms
Speaker 4: of that architecture and how fast you can move things.
Speaker 4: And now there are all sorts of companies using the
Speaker 4: NVMe protocol to move data much more fast than it's
Speaker 4: ever been done before. And so I'm saying all this
Speaker 4: Abbey as a way to kind of tee you up,
Speaker 4: to explain to the audience, like this is you can't
Speaker 4: just upgrade your existing SQL server instance to get this
Speaker 4: kind of performance. I mean, this is a whole new
Speaker 4: engine that's been built for that specific purpose.
Speaker 10: Right yeah, I mean that would be like creating mister
Speaker 10: potato Head out of wood.
Speaker 4: I mean it's like mister potato head.
Speaker 9: I forgot this can cancel culture.
Speaker 10: But anyway, you know, these legacy systems were built in
Speaker 10: a certain way to handle data in a certain way,
Speaker 10: and what they didn't expect was for that data to
Speaker 10: get so big that it's literally like busting the seams.
Speaker 9: Right right of these legacy systems.
Speaker 10: And so that's why we need new observability services that
Speaker 10: can handle this modern architecture of all these micro services dispersed.
Speaker 9: Everywhere that produce so much data.
Speaker 10: I mean, just one application produces so much data because
Speaker 10: all of those.
Speaker 9: Components that go into that app.
Speaker 10: And so that's exactly why hydraulics exists today to solve
Speaker 10: for that.
Speaker 4: Well, look these folks up online, folks, Abby Ross of Hydraulics.
Speaker 4: Very cool technology. You are listening to Inside Analysis.
Speaker 1: We expected.
Speaker 4: All right, folks back on the show here and now
Speaker 4: we're talking to Kenneth Stott. He is the field CTO
Speaker 4: from a company called Hasura, and they have a very
Speaker 4: interesting play for reporting, specifically for regulatory reporting, and they
Speaker 4: have a graph like approach to things. They use graph
Speaker 4: q well and it's sort of the last mile of data.
Speaker 4: And I really liked that concept and I that you
Speaker 4: threw out there that it's the last mile of data.
Speaker 4: And you can understand why that's important, especially for regulatory reporting,
Speaker 4: because you give all these different information systems that people
Speaker 4: are using, whether you're in financial services or healthcare, and
Speaker 4: at the end of the day, you have to deliver
Speaker 4: these reports to people and if the numbers don't line up,
Speaker 4: that doesn't look good. That's when the auditor to say
Speaker 4: no bueno, you have to go back to the drawing board.
Speaker 4: So the question is where do you where do you
Speaker 4: sort of reconcile all these different reports? And that's what
Speaker 4: Hisora does in this sort of supergraph layer. Right, tell
Speaker 4: us about that and how it works.
Speaker 2: Sure, so I would describe it as a semantic layer.
Speaker 2: What that would What I mean by that is the
Speaker 2: individual teams that to create the data and own that
Speaker 2: data publish it in semantic terms into a data access layer,
Speaker 2: and then that data access layer validates all of the
Speaker 2: relationships that they that they've described within the information that
Speaker 2: they're publishing, so that you can get across all the
Speaker 2: teams one consistent view of your data in business terms.
Speaker 2: And then that that same data access layer has a
Speaker 2: rich set of services to kind of discourage building new
Speaker 2: capabilities downstream, because oftentimes those new capabilities that they build
Speaker 2: downstream is where these problems start to arise, right sore,
Speaker 2: If they're cleaning data downstream, if they're making additional transformations,
Speaker 2: this is often where it starts to go wrong. And
Speaker 2: the other thing I want to say about this last
Speaker 2: mile of data is this is the most consequential part, right,
Speaker 2: This is where people in the c suite make key
Speaker 2: decisions that have big impact on an organization, or where
Speaker 2: regulators pick up really meaningful differences that can drive you know,
Speaker 2: a lot of pain for an organization. Putting focus on
Speaker 2: this really pays off.
Speaker 4: Yeah, that makes a lot of sense. And you know,
Speaker 4: I'm from the whole world of business intelligence and analytics
Speaker 4: and reporting, and you know, we came up with the
Speaker 4: data warehouse concept as a way of marshaling all the
Speaker 4: key data points into one relational database which we can
Speaker 4: then report from. So I get that. But to your point,
Speaker 4: a data warehouse isn't the only system of record. You
Speaker 4: have lots of other systems of record that these organizations
Speaker 4: are focused on to shepherd through. And if you have
Speaker 4: this last mile, this semantic layer, or you're tracking these things,
Speaker 4: you can get a much better handle on it. And
Speaker 4: I like the way you say it's got some built
Speaker 4: in capability to discourage downstream modifications, right, Yeah.
Speaker 2: Yeah, absolutely, data warehouses, you know, absolutely have to be
Speaker 2: part of the landscape going forward. There's a need to
Speaker 2: take data in and develop different analytics from it. But
Speaker 2: centralization has limits, and analytical data isn't the only thing
Speaker 2: that's required for these sorts of dashboards and reports. In
Speaker 2: this last mile of data, you need both operational data,
Speaker 2: you need analytical data, and so all of these things
Speaker 2: ultimately become the data products that flow into this smantic layer.
Speaker 2: There are some amazing things you can do once you
Speaker 2: have visibility to all your data as it's in motion
Speaker 2: at this last mile. Another I've studied. I've studied then
Speaker 2: a lot of quantitative studies around where things go wrong.
Speaker 2: Oftentimes anomalies have to do with people combining data across
Speaker 2: disparate domains, and you can do things like anomaly detection
Speaker 2: at that level, right, and you can say, this really
Speaker 2: doesn't make sense the way you're combining this data to
Speaker 2: produce some output.
Speaker 4: Yeah, that makes a whole heck of a lot of sense.
Speaker 4: And it is the reconciliation layer. It's the final proof really,
Speaker 4: and again you've sort of baked in here some of
Speaker 4: the formulae to be able to identify I mean, I
Speaker 4: won't it's not exactly like Master Data Management, but there
Speaker 4: is a flavor of MDN. There's a flavor of reconciling
Speaker 4: dispirit information systems. And I like too that you're not
Speaker 4: just looking at analytical data from a warehouse, you're actually
Speaker 4: pulling in operational data. As a sort of reality check, right.
Speaker 2: Yeah, yeah, absolutely, there are also upstream benefits, right, because
Speaker 2: what often happens in those data warehouses, those analytical environments
Speaker 2: is they start trying to serve a lot of different
Speaker 2: stakeholders and they start pulling in data from their peers,
Speaker 2: and you end up with a lot of cross sharing
Speaker 2: of data in ways that become problematic. So I've also
Speaker 2: studied the cost of this sort of sort of ball
Speaker 2: of yarn that we generally create in these environments, and
Speaker 2: my take on it is about two thirds of data
Speaker 2: movement and data storage is wasted. If you could optimize,
Speaker 2: you could get rid of two thirds of that cost.
Speaker 2: And by the way, costs have been going up ten
Speaker 2: percent per year for the last three years, they're projected
Speaker 2: to go up again. And if you look at data
Speaker 2: maturity statistics, they're flat. So we're spending all this money, right,
Speaker 2: but it's not getting that much better.
Speaker 4: You hit the nail on the head. And it's funny
Speaker 4: you would say that because quite literally, DM Radio. Let's see,
Speaker 4: we turned seventeen years old yesterday. That's how long we've
Speaker 4: been doing these shows, and in the earliest days in
Speaker 4: the first year, I was trying to wrap my head
Speaker 4: around all the ETL that's being done to fill the
Speaker 4: data warehouse and thinking to myself, this is crazy. You
Speaker 4: guys are clearly moving the same data multiple times. You're
Speaker 4: probably not even using eighty percent of the data that
Speaker 4: you're moving. Why are you moving it all? And it's
Speaker 4: because of how we got here. It's because way back
Speaker 4: thirty years ago, when we were beginning to really mature
Speaker 4: data warehousing architectures, people were just catching on and you're like, oh, well,
Speaker 4: I want this data in the warehouse. I want that
Speaker 4: data in the warehouse. So you get these batch windows
Speaker 4: that stack up and it's almost like an archaeological dig now, right,
Speaker 4: you have just layers and layers and layers of data movement,
Speaker 4: and the question becomes, what do we really need to do?
Speaker 4: And what you're saying is that with this supergraph layer
Speaker 4: that you've got managing semantics, if people work with you appropriately,
Speaker 4: you can solve problems upstream, meaning you don't have to
Speaker 4: move as much data now, and you can also solve
Speaker 4: a lot of problems downstream. And that's what goes to
Speaker 4: the executives to make the big decisions, right.
Speaker 2: Yeah, So the idea is that the upstreams focus i
Speaker 2: would say, on core data sets. These aren't this is
Speaker 2: information I own. I own the definition combine. You know,
Speaker 2: both the tech and the business teams together. They don't
Speaker 2: get into serving sort of ad hoc requests because somebody
Speaker 2: randomly connected with them.
Speaker 9: Right.
Speaker 2: What happens then is the supergraph layer is sort of
Speaker 2: the place where those ad hoc requests get satisfied. And
Speaker 2: that's why the upstreams stop all this sort of duplicate,
Speaker 2: you know, sharing of data because they're very focused on
Speaker 2: just what they need to do and the downstreams. The
Speaker 2: downstreams get their needs satisfied more quickly because they're able
Speaker 2: to see the data, they're able to composite it into
Speaker 2: their narrow use cases for their needs.
Speaker 5: Mm hmm.
Speaker 4: We got about two minutes, a little over two minutes
Speaker 4: left here. You had mentioned before the call that you
Speaker 4: are containerized, right, like, so you run in a Kubernetes environment.
Speaker 4: Is that correct?
Speaker 2: Yeah? So our particular product works in all the major
Speaker 2: cloud vendors of course, and you can do it as
Speaker 2: a SaaS offering, but it also works in a Kubernetes cluster,
Speaker 2: you can do it on prem and particularly think about
Speaker 2: healthcare and finance. There's a lot of consternation around public
Speaker 2: cloud and so forth, very important that you can do
Speaker 2: on prem when people have serious security concerns.
Speaker 4: Yeah. Well, I mean I have to say I think
Speaker 4: that this focus on the last mile is really crucial,
Speaker 4: and I love that you're also helping upstream by satisfying
Speaker 4: a lot of these ad hoc style queries. I mean,
Speaker 4: the bottom line is that once you get analytics in
Speaker 4: an organization, people are going to want to know things,
Speaker 4: and they're going to want to run queries and they're
Speaker 4: going to want to play with stuff. And the better
Speaker 4: you can shepherd those processes, the more governance you get,
Speaker 4: the more quality you get at the end of the day,
Speaker 4: and the low and you manage the cost as well.
Speaker 4: Right closing thoughts from you, go ahead?
Speaker 2: Yeah, absolutely, I mean I couldn't agree more. The benefits
Speaker 2: are amazing. I'll say one last thing. I don't think
Speaker 2: it's the last thing you do. You don't get your
Speaker 2: house in order and then put this layer on top.
Speaker 2: Put this layer on top for current states so you
Speaker 2: can see what the heck is going on then fix
Speaker 2: it surgically.
Speaker 4: Yeah, that's an excellent point as well. I mean it's
Speaker 4: you're talking about data observability, right. Everyone's into observability these days,
Speaker 4: into systems and you know, and tracers and logs and
Speaker 4: all these things. And I think communities is a part
Speaker 4: of that too, because it just opened up this whole
Speaker 4: can of worms about how to distribute process and everyone's like, whoa, now,
Speaker 4: hold on, how do we manage that? We how do
Speaker 4: we understand what it's doing because it's doing things very
Speaker 4: very quickly. So but this is a great focus area
Speaker 4: and it's a layer of abstraction, as you suggest, the
Speaker 4: last mile, and I like that we say, understand what's
Speaker 4: happening now and then come up with the plant if
Speaker 4: things both upstream and downstream. But look this, gentleman up
Speaker 4: online Kenneth dot of Hasura h A s U r
Speaker 4: A correct. All right, folks, you are listening to Inside Analysis.
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Speaker 16: Tune into the Faran Doozier show us you Mark the
Speaker 16: Place in Time the soundtrack to Life Sunday nights at
Speaker 16: eight pm on KCAA Radio, playing the hottest hits and
Speaker 16: the coolest conversations. Sunday nights at apm on the ferand
Speaker 16: Dozier Show with an array of music, talk, sports, community outreach,
Speaker 16: and veteran resources. With the hits from the sixties, seventies, eighties, nineties,
Speaker 16: and today's hits. The Farran Dozier Show on KCAA Radio
Speaker 16: on all available streaming platforms and on a six point
Speaker 16: five m and ten fifty Am The ferand Dozier Show
Speaker 16: on KCAA Radio.
Speaker 11: This important, time sensitive message is brought to you by
Speaker 11: this station's generous sponsor, George Let's Field Associates, who has
Speaker 11: important Medicare information for all current and future Medicare recipients
Speaker 11: about some big changes happening. Medicare Clarified. Medicare is a
Speaker 11: nonprofit consumer service organization.
Speaker 13: It's more important than ever to review your Medicare plan
Speaker 13: for twenty twenty five from October fifteenth through December seventh
Speaker 13: to find out if you're in the right plan for you.
Speaker 13: People are calling nine five one seven six nine zero
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Speaker 13: zero zero five. A popular and local Medicare plan is improving.
Speaker 13: Others are raising copays and adding deductibles, biggest changes in
Speaker 13: the Medicare drug program in fifteen years.
Speaker 11: We thank George Letzfield and lets Field Insurance for their
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Speaker 17: Here's the KCAA community calendar for March. On most Friday nights,
Speaker 17: it's open mic and karaoke in Riverside at Urrel Brewing Company,
Speaker 17: located on Chicago Avenue beginning at seven pm, So grab
Speaker 17: the mic and have some fun. In downtown Redlands on
Speaker 17: Saturday mornings from nine to one pm, it's the Downtown
Speaker 17: Morning Market located between six and eighth Street. On Saturday mornings,
Speaker 17: in the Riverside Main Library on Mission Avenue, it's Family
Speaker 17: Storytime every Saturday morning at nine thirty. For car enthusiasts,
Speaker 17: here are a couple of options to enjoy cars and
Speaker 17: coffee in downtown Clarmat on the first Saturday of the month,
Speaker 17: the Claremont Car Guys and Gals. They meet in the
Speaker 17: village for coffee and cars at six thirty am. I
Speaker 17: hope you're an early riser. In Corona, it's more cars
Speaker 17: and coffee on Saturday mornings from seven am to nine
Speaker 17: am at the IHOP on Ward Low Road. All years
Speaker 17: makes and models are welcome. This weekly meetup allows you
Speaker 17: to enjoy some beautiful cars. Looking to do some household
Speaker 17: hazardous clean up in Royale, so they offer a hazardous
Speaker 17: waste drop off on March first, March fourteenth, fifteenth, and
Speaker 17: June thirteenth from eight am till noon. The drop off
Speaker 17: site is located at two forty six South Willow And
Speaker 17: for those of you who love Pokemon in Redlands, there
Speaker 17: is a board game Teradise. Every Friday night at six
Speaker 17: thirty pm is their weekly Pokemon tournament. All skill levels
Speaker 17: are welcome, but you do need to have a full
Speaker 17: understanding of the rules and how to play. Located at
Speaker 17: one oh nine East State Street in Suite A in Redlands,
Speaker 17: and that's the latest for the KCAA community calendar. I'm
Speaker 17: Lillian Vasquez on KCAA ten fifty am and one oh
Speaker 17: six point five FM.
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Speaker 8: NBC News Radio. I'm Chris Ragio. House Republicans are unveiling
Speaker 8: a short term funding bill to keep the government running
Speaker 8: through September.
Speaker 4: Details now from Lisa Carton.
Speaker 18: The six month plan includes cuts to non defense programs
Speaker 18: and the IRS. The ninety nine page bill is in
Speaker 18: the form of a continuing resolution known as a CR,
Speaker 18: which Democrats strongly oppose. House leaders say the bill has
Speaker 18: White House support ahead of Friday shutdown deadline. Speaker Mike
Speaker 18: Johnson said this week he thinks Republicans will be able
Speaker 18: to pass it along party lines, with a floor vote
Speaker 18: expected as soon as Tuesday.
Speaker 8: The Trump administration is cutting four hundred million dollars in
Speaker 8: grants for Columbia University over pro Palestinian pro tests. Education
Speaker 8: Secretary Linda McMahon defended the move, citing anti semitism at
Speaker 8: New York City's Ivy League University. She said Jewish students
Speaker 8: have faced relentless violence, intimidation, and harassment following the deadly
Speaker 8: Hamas October seventh, twenty twenty three attack on Israel. Since
Speaker 8: last spring, the school has been a hotbed of pro
Speaker 8: Palestinian protests, which have often turned violent. An unusual standoff
Speaker 8: in London today as a man carrying a Palestinian flag
Speaker 8: has been perched on a ledge on Big Ben's Elizabeth.
Speaker 1: Tower for over ten hours.
Speaker 8: Metropolitan Police say they've received a report about the unidentified
Speaker 8: man this morning, shortly before seven thirty London time. The
Speaker 8: BBC reports the man posted a video of his climb
Speaker 8: up and said he's protesting against police repression and state violence.
Speaker 8: Area roads and tours of Parliament have been canceled as
Speaker 8: emergency crews and police negotiators try to talk them down.
Speaker 8: It's unclear if the climber is directly connected with pro
Speaker 8: Palestinian protesters who were in the vicinity for u planned rally.
Speaker 8: The Vatican says Pope Francis has shown a gradual slight
Speaker 8: improvement over the last few days. Over the weekend, the
Speaker 8: Vatican said the Pope's oxygen exchange has improved, he doesn't
Speaker 8: have a fever, and blood contests remained stable. The eighty
Speaker 8: eight year old went into a Rome hospital with